Similarly, a year ago we saw an interest in Uncertainty Quantification of Lithium-ion Batteries but wondered how to go about finding a parameter phase space of interest. The next two preprints address these two problems by finding low dimensional models and phase space within a very large number of possibilities.

Transformative applications in
biomedicine require the discovery of complex regulatory networks that
explain the development and regeneration of anatomical structures, and
reveal what external signals will trigger desired changes of large-scale
pattern. Despite recent advances in bioinformatics, extracting
mechanistic pathway models from experimental morphological data is a key
open challenge that has resisted automation. The fundamental difficulty
of manually predicting emergent behavior of even simple networks has
limited the models invented by human scientists to pathway diagrams that
show necessary subunit interactions but do not reveal the dynamics that
are sufficient for complex, self-regulating pattern to emerge. To
finally bridge the gap between high-resolution genetic data and the
ability to understand and control patterning, it is critical to develop
computational tools to efficiently extract regulatory pathways from the
resultant experimental shape phenotypes. For example, planarian
regeneration has been studied for over a century, but despite increasing
insight into the pathways that control its stem cells, no constructive,
mechanistic model has yet been found by human scientists that explains
more than one or two key features of its remarkable ability to
regenerate its correct anatomical pattern after drastic perturbations.
We present a method to infer the molecular products, topology, and
spatial and temporal non-linear dynamics of regulatory networks
recapitulating in silico the rich dataset of morphological
phenotypes resulting from genetic, surgical, and pharmacological
experiments. We demonstrated our approach by inferring complete
regulatory networks explaining the outcomes of the main functional
regeneration experiments in the planarian literature; By analyzing all
the datasets together, our system inferred the first systems-biology
comprehensive dynamical model explaining patterning in planarian
regeneration. This method provides an automated, highly generalizable
framework for identifying the underlying control mechanisms responsible
for the dynamic regulation of growth and form.

Author Summary

Developmental
and regenerative biology experiments are producing a huge number of
morphological phenotypes from functional perturbation experiments.
However, existing pathway models do not generally explain the dynamic
regulation of anatomical shape due to the difficulty of inferring and
testing non-linear regulatory networks responsible for appropriate form,
shape, and pattern. We present a method that automates the discovery
and testing of regulatory networks explaining morphological outcomes
directly from the resultant phenotypes, producing network models as
testable hypotheses explaining regeneration data. Our system integrates a
formalization of the published results in planarian regeneration, an in silico
simulator in which the patterning properties of regulatory networks can
be quantitatively tested in a regeneration assay, and a machine
learning module that evolves networks whose behavior in this assay
optimally matches the database of planarian results. We applied our
method to explain the key experiments in planarian regeneration, and
discovered the first comprehensive model of anterior-posterior
patterning in planaria under surgical, pharmacological, and genetic
manipulations. Beyond the planarian data, our approach is readily
generalizable to facilitate the discovery of testable regulatory
networks in developmental biology and biomedicine, and represents the
first developmental model discovered de novo from morphological outcomes by an automated system.

Renewable energy researchers use computer simulation to aid the design of
lithium ion storage devices. The underlying models contain several physical
input parameters that affect model predictions. Effective design and analysis
must understand the sensitivity of model predictions to changes in model
parameters, but global sensitivity analyses become increasingly challenging as
the number of input parameters increases. Active subspaces are part of an
emerging set of tools to reveal and exploit low-dimensional structures in the
map from high-dimensional inputs to model outputs. We extend a linear
model-based heuristic for active subspace discovery to time-dependent processes
and apply the resulting technique to a lithium ion battery model. The results
reveal low-dimensional structure that a designer may exploit to efficiently
study the relationship between parameters and predictions.